remove-ai-watermarks/scripts/README.md

Maintainer scripts

These scripts are development and evaluation tools, not installed CLI commands. Run them from the repository root with uv run python scripts/<name>.py --help. Inputs under .local-eval/ and generated reports remain untracked unless a data README explicitly names a tracked canonical result.

Audits and release checks

Script	Purpose
corpus_gap_scan.py	Compare a local image corpus with the library's identify results.
detection_timing.py	Record per-method metadata and verdict timings.
detection_timing_report.py	Aggregate timing records by method and segment.
fidelity_metrics.py	Compute objective image-fidelity metrics for paired outputs.
invisible_quality_audit.py	Pair originals and invisible-removal outputs for quality review.
metadata_removal_audit.py	Check metadata detection/removal parity over a corpus.
pill_gate_audit.py	Measure the Jimeng pill detector on the product path.
real_examples_e2e.py	Run end-to-end confidence checks over local examples.
record_parity_audit.py	Compare record-based and file-based identification.
resource_ceilings.py	Measure peak RSS and runtime for fill backends.
robustness_suite.py	Exercise CLI failures on adversarial and degenerate inputs.
sidecar_regression.py	Compare current identification with recorded sidecars.
smoke_matrix.py	Exercise CLI parameter choices on real local data.
video_fidelity_probe.py	Compare delivered video fidelity with its source.
visible_eval.py	Benchmark registered visible-mark detectors.
visible_removal_audit.py	Audit visible-removal results over a local corpus.

Calibration and corpus preparation

Script	Purpose
contentseal_transforms.py	Reproduce and hash-check deterministic Content Seal variants.
detector_response.py	Measure detector response over mark size, contrast, background, and aspect.
fill_quality.py	Measure visible-fill quality against constructed ground truth.
ladder_headroom.py	Measure recall cost from the coarse scale ladder.
registered_mark_calibrate.py	Measure a registered detector without conflating visual positives, metadata cohorts, adjudicated negatives, and unlabeled controls.
synthid_corpus.py	Ingest and inspect the local SynthID reference corpus.
vendor_cohort_harvest.py	Partition TC260 carriers by producer code.
vendor_mark_calibrate.py	Calibrate a candidate vendor text detector.
visible_alpha_solve.py	Rebuild visible-watermark alpha assets from controlled captures.
visible_groundtruth.py	Consolidate blinded contact-sheet labels into ground truth.
visible_positives.py	List corpus images carrying a registered visible mark.
visible_recall_sample.py	Build an unbiased blinded sample for recall measurement.
visible_sheets.py	Build blinded contact sheets for relaxation candidates.
publish_photo_classify_hf.py	Upload the photo-classify card and freeze weights to Hugging Face wiltodelta/raiw-models. Manual; the Action publish-photo-classify-hf.yml is the write-token path.

Research and diagnostic prototypes

Script	Purpose
cjk_tail_probe.py	Test a generic template for otherwise uncovered CJK labels.
controlnet_sweep.py	Sweep the historical ControlNet removal prototype.
infer_text_lines.py	Draft stable source-text lines without modifying pixels.
qwen_scrub_prototype.py	Probe low-strength Qwen regeneration on a GPU.
selective_text_restoration.py	Evaluate text restoration over a scrubbed image.
synthid_pixel_probe.py	Run the experimental local SynthID carrier probe.
video_synthid_sweep.py	Build oracle-gated video regeneration candidates.

Generated assets

Script	Purpose
render_pill_silhouette.py	Render the synthetic Jimeng pill silhouette.
render_vendor_silhouettes.py	Render synthetic vendor text-mark silhouettes.

Shared helpers

_plain_console.py provides plain-text fallbacks for Rich output, and _text_eval.py contains normalization helpers shared by text-evaluation scripts.